CN117203593A - Method and system for measuring a component, and program - Google Patents

Abstract

The invention relates to a method and a system for measuring a component (B) produced by a production plant (1), the method comprising: a) selecting (S1) a component (B) to be measured from a plurality of components (B), the selection being made in accordance with at least one selection parameter (p), B) creating (S2 a) component-specific Measurement Data (MD) by measuring the selected component (B) using a coordinate measuring machine (2) and evaluating (S2B) the Measurement Data (MD), and/or c) determining (S3) at least one production parameter (m), d) adjusting (S4) the at least one selection parameter (p) on the basis of the evaluation result (r) and/or on the basis of the production parameter (m) or a change in the production parameter (m). And the present invention relates to a program.

Description

Method and system for measuring a component, and program

The present invention relates to a method and a system for measuring a component, and a program.

In the industrial manufacture of components or workpieces, it is often important to monitor the quality of the components, above all in order to ensure quality assurance, and also in order to detect possible production defects in time. However, monitoring quality requires a certain amount of time and thus reduces yield.

The test plan for quality assessment includes definitions of one or more of the following aspects: test characteristics, test procedures, test frequency (sampling frequency), test method, test means and test data processing.

The test plan for evaluating the design may in particular comprise a test signature analysis for the purpose of deriving a measurement task for each measuring element, which may be a shaped element, in particular a shaped element to be probed. Such analysis may include selection of test signature analysis, analysis of connections between the forming elements, deriving measurement elements from the forming elements, and forming elements required to formulate measurement tasks on the forming elements/measurement elements. Measurement programming and the creation of probing and evaluation strategies can then be performed on the basis of this analysis.

For example, the test feature may be the diameter of a bore in the component, and the measurement element may be the cylindrical surface of the bore. The number and distribution of measuring points on/at the measuring element can then be defined by a detection strategy, for example measuring points along a circular line along the surface of the cylinder.

If the test criteria, in particular test feature-specific test criteria, have been met, the component may be quality controlled or partially quality controlled. For example, the standard test criteria may be met if the diameter of the borehole does not deviate more than a predetermined amount from a predetermined target value. One form of quality control may be based, for example, on manual definition of test criteria by a user, e.g., based on empirical values from the user.

It is also known to adjust the production process based on the measured dimensions of the workpiece features. This is disclosed, for example, in WO 2019/122821 A1, the teachings of which relate to the production and measurement of workpieces or parts.

DE 102 42 A1 discloses a quality assurance method in which an error message and/or alarm is output if a stored actual value measured on a component deviates from a specific target value.

Also known from the prior art is EP 19,215, 250.2, which document does not disclose and discloses a coordinate measuring method and a coordinate measuring machine for a plurality of workpieces, wherein at least one unstable test feature and at least one stable test feature are determined or assumed and the plurality of workpieces are measured, wherein the at least one unstable test feature is measured more frequently than the at least one stable test feature.

In general, when evaluating the quality of a plurality of components, a problem arises as to how many of these components must be tested and which test features of these components in order to be able to make quality assurance reliable but at the same time require very little time.

The technical problem that arises is to develop a method, a system and a program for measuring a component, which method, system and program enable reliable quality assessment while reducing the time required for said quality assessment.

The solution to the technical problem is provided by the subject matter having the features of the independent claims. Further advantageous configurations of the invention are evident from the dependent claims.

A method for measuring a component (i.e. a plurality of components) is presented. The method makes it possible in particular to determine or change the measurement strategy of the measuring component and then to make further measurements in accordance with the determined/changed measurement strategy.

The component is produced by a production facility. In particular, the production facility may be or include a manufacturing or processing apparatus. Thus, the production apparatus may perform at least one production and/or processing step during the production of the component. The component may also be a workpiece within the meaning of the invention.

The method includes selecting a part to be measured from a plurality of parts. For example, the selection may be made from a batch or batches of parts, a batch comprising a predetermined plurality of parts, e.g. produced under the same production conditions. However, it is also possible to select from a plurality of parts produced in a series production. In particular, the plurality of components may comprise more than 10, more than 50 or more than 100 components.

The selection is made based on at least one selection parameter. This may mean that the selection is characterized by at least one selection parameter. As will be explained in further detail below, such a selection parameter may be, for example, the sampling frequency. The selection parameter may also be an ordinal number in the sequence of components. The selection parameters may define how many components should be measured and optionally which of the plurality of components should also be measured.

The measurement of the component may be used for quality control/evaluation in the production process. In particular, it may be checked whether each selected component has fulfilled at least one quality criterion. In this context, the components may each include or have at least one test feature. Further, the component may be measured and tested according to a test plan, wherein the quality assessment is performed based on measurement data created according to the test plan. In this case, at least one test criterion of the quality assessment may be defined by a test plan. The test criteria may be test feature specific test criteria. For example, a test may be performed during evaluation of the test criteria as to whether the test feature has a target characteristic (e.g., a target size) or deviates from the target characteristic by more than a predetermined amount. In which case the test criteria may be met. A component may pass a quality assessment if the component meets all or a predetermined number of test criteria, that is, for example, if one or more characteristics determined by measurement do not deviate from a predetermined target characteristic or deviate from a predetermined target characteristic by more than a predetermined amount. If this is not the case, the component fails the quality assessment.

The method further includes creating component-specific measurement data by measuring the selected component using the coordinate measuring machine and analyzing or evaluating the measurement data.

In this context, the coordinate measuring machine may comprise at least one sensor for creating measurement data. The sensor may be a tactile sensor or an optical sensor, or any other sensor. Such coordinate measuring machines and sensors for creating measurement data during measurement are known to those skilled in the art. In this case, the (component-specific) test plan may define which measurement points should be measured to create component-specific measurement data, or at which measurement points measurement data is created.

The measurement data may be evaluated to determine whether the component has met one or more test criteria for quality assessment. Furthermore, an evaluation of the measurement data may be performed to determine a component-specific property, such as a variable, more specifically a dimension, of the test feature. The measurement data may also be evaluated to determine resulting characteristics of one or more component-specific variables of the plurality of components. For example, there may be a statistical evaluation as an evaluation result of the measurement data, determining statistical variables of the plurality of components, such as the mean or divergence of the component-specific characteristics. The evaluation result and/or the result of the quality evaluation of the specific properties of these components, the resulting properties or the test criteria may be an evaluation result.

Further, the adjustment of the at least one selection parameter is performed based on the evaluation result. Such adjustment may also be referred to as adjustment by closed loop control according to a small or internal control loop. For example, if a predetermined evaluation result is determined, the selection parameter may be set to a predetermined value or changed in a predetermined manner. In this context, there may be a result specific adjustment. For example, the specific result may be assigned a predetermined value of the selection parameter or a predetermined change of the selection parameter, such as an increase or decrease.

As a result, it is advantageous to choose properties that are adapted to the component produced. In turn, this makes it possible to reduce the amount of time required for quality assessment of the plurality of components. For example, if it should be determined that the characteristics of all selected component or components either correspond to predetermined characteristics or deviate from them by no more than a predetermined amount, the selection parameters may be adjusted, e.g., the sampling frequency may be reduced. The selection parameter may also be adjusted if the resulting characteristic corresponds to a predetermined target value or deviates from the predetermined target value by no more than a predetermined amount. The selection parameters may also be adjusted if all selected components or a plurality of selected components pass the quality assessment.

In the alternative or cumulatively, the production parameters are determined. In particular, the production parameters may represent production conditions. Exemplary production parameters are still explained in detail below. Further, the adjustment of the at least one selection parameter may alternatively or cumulatively be performed based on the production parameter or a change in the production parameter. In particular, it is therefore no longer necessary to select each component and to measure the component after a change in production parameters for quality assessment purposes. Such adjustment may also be referred to as adjustment by closed loop control according to a large or external control loop.

Such a production parameter-dependent adjustment can be carried out with a time offset. For example, the selection parameter may be adjusted when measuring a first component produced using the production parameter determined according to the invention or when making the first component available for measurement. In other words, the necessity of an adjustment can thus be detected on the basis of the production parameter or a change thereof, wherein the adjustment is only carried out after a predetermined period of time after the necessity has been detected, that is to say in particular not immediately. For example, this time period may be equal to the time period between the completion of the production of one component (in particular using the production parameter or the changed production parameter) and the subsequent measurement by the coordinate measuring machine or the subsequent selection of a component for the purpose of the measurement. For example, one or more components may be produced during production using at least one production parameter, wherein the selection of the component to be measured from the group of produced components is performed only after a predetermined period of time after completion of the production, depending on the at least one selection parameter (which selection parameter is adjusted in a manner dependent on the production parameter), wherein the component is temporarily stored and/or transported, for example from the production equipment to the coordinate measuring machine, for example during the predetermined period of time. This enables the production facility and the coordinate measuring machine to be spatially separated, for example at different locations within the factory floor or at different factory floors, wherein the selection of the components for measurement can still be made in a manner adapted to the production conditions.

Thus, in particular, an adjustment may be performed if at least one production parameter assumes a specific value or changes beyond a predetermined value. This value or predetermined amount may be a parameter specific value, a parameter specific amount. If the production parameters do not change or if the change in the production parameters is less than or equal to a predetermined amount, there may be no adjustment of the production parameters.

In other words, the production parameters or variations thereof thus have an influence on the selection process of the component to be tested. For example, if the production parameters change, in particular in a predetermined manner, the sampling frequency can thus be increased.

The component to be measured can be further selected according to the adjusted selection parameters. In particular, the selection may be made in accordance with the adjusted selection parameters immediately after the adjustment of the selection parameters.

The selection may be made in accordance with at least one predetermined initial selection parameter prior to the first adjustment. The predetermined initial selection parameters may be specified, for example, by a user, for example, based on empirical knowledge. Alternatively, each component may be selected prior to the first adjustment. In the following, the adjustment can then be carried out continuously, i.e. repeatedly. Thus, the selection parameters are thus subject to closed loop control.

Thus, a reliable but at the same time fast quality evaluation of the components produced under the changed production conditions can be ensured, and thus, in particular, also the yield of components produced within a predetermined period of time can be improved, and these components can be considered to meet the quality requirements. This may also change the specifications as to how many and optionally which of the plurality of components should be measured for reliable quality assessment. The closed-loop control can also be dynamically adjusted to adapt the quality assessment process to changing production conditions and/or current component characteristics quickly and reliably.

For example, in the event of certain changes in certain production parameters, the sampling frequency may be increased or decreased. However, if the production conditions have not changed, the sampling frequency may be kept at the set value, or alternatively lowered according to the evaluation result, which in turn reduces the period of reliable quality evaluation. In other words, it is thus conceivable to adjust at least one selection parameter, in particular within a predetermined period of time, in such a way that fewer components to be measured than before are selected if one or more production parameters, in particular the selected production parameter, do not change or change by more than a predetermined amount. This can be implemented in particular by reducing the sampling frequency. In particular, such an adjustment may be implemented if, in addition, an evaluation of the measurement data created during the measurement of the component produced with this production parameter/parameters yields that the characteristics of the component selected before the adjustment correspond to the predetermined characteristics or do not deviate from the predetermined characteristics by more than a predetermined amount.

In another embodiment, at least one measurement parameter is determined, the at least one selection parameter being adjusted based on the measurement parameter or a change in the measurement parameter. The measurement parameter may represent a measurement condition when the component is measured. Such measurement parameters may represent, for example, the sensors used or the test devices used for the measurement. The measurement parameter may also be a measurement temperature. In this case, the explanation concerning the adjustment of the selection parameters based on the production parameters is also applicable in a corresponding manner to the adjustment of the selection parameters based on the measurement parameters. For example, in the event of certain changes in certain measured parameters, the sampling frequency may be increased or decreased. For example, if the sensor for measurement is replaced with a sensor of lower accuracy, the sampling frequency can be increased. However, if the measurement conditions are not changed, the sampling frequency may be kept at the set value, or alternatively lowered according to the evaluation result, which in turn reduces the period of reliable quality evaluation.

In a preferred embodiment, at least one selection parameter is adjusted in a partially or fully automated manner. Fully automated may particularly mean that the adjustment is performed without user interaction (e.g. user input), further without user confirmation or input of e.g. a value of the selection parameter. In particular, the selection parameters may be adjusted by means of a manual system or by means of artificial intelligence methods.

Partially automated adjustment of the selection parameters is also conceivable. In this case, the selection parameters may be determined without any user input or interaction and presented to the user for confirmation, e.g. using a suitable output device. The user may then prompt for an adjustment of the selection parameters by means of confirming the proposed selection parameters, for example by operating a suitable input device.

For example, the selection parameter intended for adjustment may be determined by evaluating a specific mapping between the evaluation result and/or the production parameter and/or the variation thereof and the selection parameter or by evaluating a predetermined mapping of the evaluation result and/or the production parameter and/or the variation thereof to the variation of the selection parameter.

For example, the predetermined map/maps may be evaluated by a suitable evaluation device and the adjustment is performed by means of using the selection parameters generated by the map or changing the currently set selection parameters according to the changes generated by the map.

This advantageously results in a fast and reliable adjustment of the selection parameters, in particular in accordance with changing production conditions and/or characteristics of the component. This in turn enables reliable quality assessment requiring little time.

In another embodiment, the selection parameter is adjusted based on rules. For example, the rule-based adjustment may be carried out by evaluating the predetermined rule, in particular by means of a data processing method. In this case, the evaluation result and/or the production parameter and/or the change in the production parameter as explained above may form an input variable of the rule. The output variable of the rule may be at least one selection parameter to be set by adjustment or a change of said selection parameter.

Thus, a rule may represent a relationship between exactly one input variable or more than one input variable and exactly one output variable or more than one output variable.

The rules thus form a mapping between at least one input variable and at least one selection parameter or a variation of at least one selection parameter.

In this case, the rule may be specified by the user. In particular, the rules may be specified by the user, for example for this purpose, for which empirical values may be taken. Rules may then be defined by appropriate user input, for example. In other words, the expertise may be reflected by rules.

Alternatively, the rules may be determined based on the evaluation, in particular interpretation, of the measurement and production data. In particular, the statistical data evaluation method may be applied to a dataset comprising measurement and production data in order to identify a relationship, in particular a relationship between a change in production conditions and a change in the evaluation result of the measurement data. In particular, statistical features may be determined and used to determine rules. To this end, the data set may also comprise evaluation results. Statistical methods may particularly include or apply data compression methods.

The user-specified rules or rules determined by evaluation may be stored in a storage device of a system for measuring components, for example. Further, the stored rules may be evaluated in particular by the evaluation and control device of the system.

This enables as reliable a rule determination as possible, in particular based on relationships that are not immediately obvious to the user, and thus also an improved adjustment of the selection parameters according to changing production conditions and/or component properties. In other words, this enables additional expertise to be generated in the form of these rules.

In this case, the production data may be data representing production conditions of the component. In particular, the production data may comprise or encode one or more production parameters. In particular, a data mining method may be applied to derive rules for rule-based adjustment from the measurement and production data that has been created.

For example, if a change in production conditions (e.g., represented by a change in production parameters) results in a decrease in quality of the component produced after the change (which can be determined by evaluation of the measurement data), the rule may be defined in such a way that the sampling frequency is increased when a change in production parameters is detected. For example, if a change in production conditions (e.g., represented by a change in production parameters) results in an improvement in the quality of the component produced after the change (e.g., because the worn tool is replaced with a new tool), the rules may be defined in such a way that the sampling frequency is reduced when a change in production parameters is detected.

The use of rules for adjusting the selection parameters advantageously results in an easy-to-implement adjustment of the selection parameters.

In another embodiment, the rules are determined by machine learning. In this case, the term "machine learning" includes or represents a method for determining rules based on training data. Thus, rules, in particular in the form of models, can be determined by means of supervised learning methods, and training data comprising the input and output variables explained above can be used for this purpose. In this case, the input and output variables forming the training data may be specified by the user, for example, as the user-specified rules explained above. In the alternative, or cumulatively, the input and output variables of the rules determined by evaluation and explained above may form training data or a part thereof.

For example, a user may define a particular value of an input variable or a particular change in an input variable to produce a particular value of an output variable or a particular change in an output variable. Further, training, i.e. model recognition or recognition of one or more rules, may be implemented in this way based on user-provided data. However, it is naturally also conceivable to use an unsupervised learning method to determine the rules.

Mathematical algorithms suitable for machine learning include: decision tree-based methods, integrated-based methods (e.g., boosting, random forest), regression-based methods, bayesian-based methods (e.g., bayesian belief networks), kernel-based methods (e.g., support vector machines), instance-based methods (e.g., k-nearest neighbor algorithms), association rule learning-based methods, boltzmann machine-based methods, artificial neural network-based methods (e.g., perceptron), deep learning-based methods (e.g., convolutional neural networks, stacked auto-encoders), dimension-reduction-based methods, regularization-based methods.

Determining rules through a neural network is also conceivable. For example, the neural network may be in the form of an automatic encoder or in the form of a Convolutional Neural Network (CNN) or in the form of an RNN (recurrent neural network) or in the form of an LSTM network (long short term memory network) or in the form of a neural transformation network or a combination of at least two of the foregoing networks. Artificial intelligence methods may also be applied to determine rules. These are well known to those skilled in the art.

This advantageously results in an improved adjustment of the measurement of the component to the production conditions, in particular to the development of the production conditions over time, wherein at the same time the aforementioned reliability and accuracy of the quality assessment are ensured. Thus, the machine learning method in particular allows to determine rules that can be derived from the relationships between the input variables and the output variables as explained above, which the user finds difficult to identify.

Rules may also be adjusted. This may mean that the rule, in particular at least one rule parameter of the rule, is changed. For example, the model explained above may be adjusted. In this case, the adjustment can also be implemented by a machine learning method. Rules, in particular the model explained above, can also be adapted by means of an adaptive algorithm.

In this case, it is conceivable to evaluate measurement and production data which also serve as a basis for determining rules in order to create new rules and/or to change existing rules. Suitable machine learning methods may be used for this purpose. In addition, the already trained model may be retrained with newly provided training data, for example in the form of input and output variables, of rules determined by evaluation, wherein the rules determined by evaluation are determined by evaluating measurement and production data created with previously unadjusted models.

By adjusting the rules, it is advantageously possible to measure permanent, in particular continuous, adjustments of the development over time of the characteristics according to the production conditions and/or the produced components, and with this, the already mentioned reliability and accuracy guarantees of the quality assessment.

In another embodiment, the production parameter is or represents an environmental condition. In particular, the environmental conditions may be (production) temperature, (production) air pressure, (production) humidity and/or brightness, which influence or are decisive for the production.

Furthermore, the production parameters may be or represent tools for production. In this case, the change in the production parameter may be or represent a change in the tool. For example, if the tool is replaced with a less used tool, the sampling frequency may be reduced. Furthermore, the sampling frequency can be increased if the tool is replaced with a tool that is subject to more wear.

Furthermore, the production parameters may represent a method for production. For example, it is conceivable that the component may be produced by different production methods. For example, the desired surface shape of (a part of) the component may be produced by milling methods, grinding methods, planing methods or any other manufacturing method. In some cases, different manufacturing qualities of such surface shapes may be obtained using different methods. For example, if production is switched from one manufacturing method to a manufacturing method with a relatively high manufacturing quality, the sampling frequency can be reduced.

Further, the production parameter may be or represent the number of parts produced from a certain time. For example, the production parameter may be the number of parts produced since the last implemented selection parameter adjustment or the number of parts produced since the start of production, since the start of shift, or since the start of batch production.

Further, the production parameter may be or represent a production time period since a certain time. For example, the production parameter may be a production time period since a last implemented selection parameter adjustment or a time period since a start of production, a start of shift, or a start of batch production.

For example, the selection parameters may be adjusted after production of a predetermined number of components and/or after expiration of a production time period, in particular but not necessarily under unchanged or approximately unchanged production conditions, such as increasing the sampling frequency. In this case, the condition that is approximately unchanged means a condition that does not deviate from the condition existing at the beginning by more than a predetermined amount.

Further, the production parameter may be or represent the number of batches produced from a certain time. In this case, the production parameter may be, for example, the number of batches produced since the last adjustment performed or the number of batches produced since the start of production or since the start of a shift. For example, if a predetermined number of component batches are produced, particularly but not necessarily under constant or approximately constant production conditions, adjustments may be made, such as increasing the sampling frequency. In particular, the predetermined number of batches may be 1.

In this case, the start of the production, shift or batch may be in particular the start time of the production, shift or batch during which the component that should be selected according to the selection parameters is produced.

Further, the production parameters may represent a shift group or shift group change, such as an early shift, a normal shift, a late shift, a night shift, or any other shift group, or a shift change between shifts, such as the number of shift changes since the start of production or the start of batch production or since the start of a shift of a scheduled shift. In this case, if a shift change occurs or if a plurality of shift changes occurs, the production parameters may be changed, for example, the sampling frequency may be increased. Thus, the selection parameters may be adjusted when a shift change occurs or if multiple shift changes occur.

The production parameters may further also represent test means used during production or for production, and thus test means before selection and measurement by means of a coordinate measuring machine, wherein, for example, the production process is subjected to open-loop or closed-loop control based on the test results of the test means.

Further, the measurement parameter may represent a sensor for measurement. This has been explained above.

The listed production parameters advantageously allow reliable adjustment of the measurement according to production conditions, variations in production conditions, as the rules have an impact on the production quality of the produced component. For example, it has been recognized that changes in one or more of the aforementioned environmental variables, particularly changes exceeding a predetermined amount, can alter production quality. For example, it is recognized that an increase in the number of parts produced since a certain time or an increase in the production time period or an increase in the number of production lots may have an impact on the production quality, particularly in view of tool wear. For example, the sampling frequency can thus be increased in the event of a change which leads to a reduction in quality.

However, it is also recognized that variations may also lead to improvements in quality. For example, if more accurate and/or less used tools are used, this may improve production quality. For example, the sampling frequency can thus be reduced in the event of a change which causes an increase in quality.

It is also recognized that the shift group may have an impact on production quality. Thus, the sampling frequency can be increased in case of shift changes in order to ensure that the desired quality is ensured even during production of a new shift.

In this case, the production parameter value or the change in the production parameter that causes the reduction in production quality or the improvement in production quality can be identified by the measurement and the evaluation of the production data as explained above. Further, the reduction or increase or the value resulting from the reduction or increase may be assigned to the selection parameter, for example in the form of a predetermined map. This mapping then allows to determine the selection parameters that should be used for the adjustment.

In another preferred embodiment, the selection parameter is the sampling frequency. In this case, the sampling frequency may represent the number of measured components in a predetermined set of components (e.g. 100) or in a set of components produced within a predetermined time, in particular measured for quality assessment. The higher the sampling frequency, the greater the time and computational expenditure required for the corresponding measurement and test. However, higher sampling frequencies also improve the reliability of the quality assessment.

The selection of the parameters as sampling frequency advantageously enables the adjustment of the measurement to be carried out very easily while ensuring the desired reliability and accuracy of the quality assessment.

In another embodiment, the at least one selection parameter or the at least one further selection parameter is an ordinal number in the sequence of produced components. For example, the sequence may be a sequence of parts produced since the last adjustment of the selection parameters, since the start of production, since the start of batch production, since the start of shift, or since a predetermined time in the past.

The plurality of ordinals may be defined by a plurality of selection parameters. For example, the ordinal numbers may be equally distributed between the first and last elements of the sequence, particularly between the first and last elements of the plurality of elements from which the selection is made. In other words, for example, every nth component can be selected, where n=1, 2,3, …. However, the ordinal numbers may also be unevenly distributed over the sequence, that is to say not equally spaced in sequence between the first and the last produced component. As a result, every kth component may be selected in the first section of the sequence, e.g. up to a predetermined number of components in the sequence. Then, in the subsequent section, each tth component may be measured, wherein t may be different from k, in particular greater than k.

This advantageously allows frequent measurements and quality evaluations to be carried out, for example, at the beginning of production, with changing production conditions, whereby the desired reliability and quality of the quality evaluation can be ensured from the beginning. For example, it may be the case that components produced immediately after a change in production conditions are more prone to undesired quality deviations than later produced components, for example due to transients in the corresponding production equipment.

In another embodiment, the part to be measured is selected from a batch of parts. In this case, for example, the sampling frequency may be related to the number of parts in the lot. Further, a component to be measured is selected from another lot of components (e.g., a lot produced later) according to the adjusted selection parameters. This therefore results in a lot-specific component selection and evaluation of the corresponding measurement data. Thus, there is a lot-specific adjustment of the selection parameters. This advantageously represents a good compromise between the tuning frequency and the desired reliability and accuracy of the quality assessment, in particular because it can be assumed that the same batch of components is produced under substantially the same production conditions.

Naturally, however, it is also possible to select the component to be measured from a (first) subset of components produced during the series production, wherein the selection parameters are then optionally adjusted, wherein the component to be measured is then selected from another subset of components of the series production according to the adjusted selection parameters, wherein the components of the other subset are produced later than the components of the first subset. This results in a continuous or almost continuous adjustment of the measurement and thus also in a quality assessment.

In a further embodiment, the adjustment of the at least one selection parameter is carried out by evaluating on the basis of the quality measure or a change in the quality measure, at least one quality measure of a plurality, in particular a predetermined number but not all, or of each of the selected components being determined. In this case, the quality measure may represent the quality of one or more components. For example, a ratio between the number of selected parts and the total number of selected parts through quality assessment may be determined as a quality measure.

In particular, the selection parameters can then be adjusted in such a way that a reliable quality evaluation of the plurality of components is ensured by the measurement of the components selected according to the adjusted selection parameters and the quality evaluation based on the present measurement.

For example, if the quality (that is, the quality represented by a quality measure, for example) decreases more than a predetermined amount within a predetermined period of time, the sampling frequency may be increased. For example, if the quality does not decrease more than a predetermined amount within a predetermined period of time, the sampling frequency may be decreased.

This advantageously results in a measurement, in particular a selected adjustment, in such a way that reliable quality assurance can be implemented with as few components as possible being measured.

In a further embodiment, at least one component-specific characteristic of the or each selected component is determined by evaluation, wherein the adjustment is performed if a predetermined number of components, for example one or more or all components, deviate from a target value, in particular a predetermined target value, by more or less than a predetermined amount, or if a component-specific characteristic changes by more than a predetermined amount. Such deviations or variations may be quality measures. The characteristic may be a variable, in particular

Dimensional variables such as the dimensions of the test features. In this case, the dimension may be, for example, a length, a width, a depth, a diameter, a circumference, a distance, or another dimension variable.

For example, in the case of a predetermined number of components, the sampling frequency may be increased if the component-specific characteristic deviates from the target value by more than a predetermined amount, or the sampling frequency may be decreased if the component-specific characteristic does not deviate from the (other) target value by more than a predetermined amount in the case of a predetermined number of components.

In an alternative, at least one resulting characteristic is determined on the basis of the component-specific characteristic determined by the evaluation, and the adjustment is carried out if the resulting characteristic deviates from the target value, in particular the predetermined target value by more or less than a predetermined amount, or if the resulting characteristic changes by more than a predetermined amount. The resulting characteristic may be, for example, a mean or a divergence. Further, the resulting characteristic may be a defect frequency related to the number of selected parts (e.g., parts in a lot). For example, this may specify the number of defective parts or defects that are generally detected in the number of selected parts.

More generally, the resulting characteristic may be a statistical variable representing or characterizing the divergence of the particular characteristic of the component. The resulting characteristic or deviation of the resulting characteristic from the target value may be a quality measure. For example, the sampling frequency may be increased if the resulting characteristic deviates from the target value by more than a predetermined amount, or the sampling frequency may be decreased if the resulting characteristic deviates from the target value by less than a predetermined (further) amount.

Advantageously, this results in a change in the measured value of the component if the characteristic, which is particularly critical for quality, changes by more than a desired amount. In particular, in the alternative scenario described above, the sampling frequency may be increased and/or the ordinal number or ordinals of the component to be selected may be adjusted for a subsequent sequence of produced components. If such a characteristic does not change or changes less than a desired amount, the sampling frequency may be reduced, optionally likewise the number of components to be selected may be adjusted, for example. Thus, a rapid reaction can be made to adverse changes in variables critical to quality, thereby in turn ensuring a reliable and accurate quality assessment.

After determining the at least one production parameter, in another embodiment the selection parameter is set to a value assigned to the production parameter or to a change in the production parameter. Alternatively, the selection parameter is changed, wherein the change is assigned to the production parameter or a change in the production parameter. For this purpose, a predetermined mapping or rule can be evaluated. This and corresponding advantages have been explained above.

In this case, the adjustment may include a determination of a selection parameter or a change thereof. Further, the adjusting may also include changing the current setting of the selection parameter.

In a further embodiment, the purely production parameter-dependent adjustment of the at least one selection parameter is followed by

a) Selecting a component to be measured from a predetermined number of components, the selection being made in dependence of a selection parameter which has been adjusted in a manner related to the production parameter,

b) Creating component-specific measurement data by measuring the selected component using a coordinate measuring machine and evaluating the measurement data, and

c) And updating at least one selection parameter based on the evaluation result, and adjusting the selection parameter in a purely result-dependent manner.

In this case, an adjustment purely related to a production parameter means an adjustment performed based on the production parameter or a change in the production parameter, not based on the evaluation result. Accordingly, a purely result-dependent adjustment means an adjustment that is implemented based on the evaluation result, not on the production parameter or a change in the production parameter.

In other words, the production parameter related variation of at least one selection parameter is followed by a selection using the selection parameter corresponding to the variation and an adjustment based on the evaluation result instead of on the production parameter is optionally performed thereafter. In other words, a check can be made after a change in relation to the production parameters as to whether further changes in the selection parameters are required in order to ensure a reliable and accurate quality assessment, for example because the manner in which the characteristics of the component change is that a reliable quality assurance requires a higher sampling frequency or a constant reliable quality assurance allows a lower sampling frequency.

Advantageously, this allows a reliable and accurate adjustment of the at least one selection parameter.

Further a program is proposed which, when executed on or by a computer, prompts the computer and the coordinate measuring machine to perform one or more or all steps of a method for measuring a component according to one of the embodiments explained in this disclosure. In the alternative or cumulatively, a program storage medium or computer program product is described, on or in particular in a non-transitory (e.g. permanent) form. In the alternative, or cumulatively, a computer comprising such a program storage medium is described. In another alternative or cumulatively, a signal (e.g. a digital signal) is described, which encodes information representative of a program and comprises encoding means adapted to perform one or more or all of the steps of the measurement method set forth in this disclosure. The signal may be a physical signal (e.g., an electrical signal), which is in particular technically or by machine generated. The program may also prompt the computer to use the coordinate measuring machine to make measurements of the component, particularly the selected component.

Further, the measurement method may be a computer-implemented or at least partially computer-implemented method. For example, one or more or all of the steps of the method may be performed by a computer, except for creating measurement data. One embodiment of the computer-implemented method is to use a computer to perform the data processing method. The computer may for example comprise at least one computing device, in particular a processor, for example at least one storage device, in order to process the data, in particular technically, for example electronically and/or optically. In this case, the computer may be any type of data processing device. The processor may be a semiconductor-based processor.

This advantageously results in a procedure by means of which the method explained above with the same explained advantages can be implemented.

A system for measuring a plurality of components produced by a production facility is also presented. The system includes at least one coordinate measuring machine and at least one evaluation and control device. In this case, the evaluation and control device may comprise a microcontroller or an integrated circuit, or be embodied as such. The system is configured to perform the method of one of the embodiments explained in this disclosure. The system may also include a production facility.

In this case, the evaluation and control device can be connected in data and/or signal to a control device for controlling the operation of the coordinate measuring machine. The evaluation and control device can also be connected to the production device in a data and/or signal connection.

In this case, the control and evaluation device

The selection of the component to be measured and the evaluation of the measurement data can be carried out,

can drive the coordinate measuring machine to create measurement data, and/or

Can determine the production parameters and/or changes thereof, and

adjustment of at least one selection parameter may be performed.

In this case, the evaluation and control device may be composed of or include or be formed of a plurality of modules. The three-coordinate measuring machine may be driven based on the test plan.

This advantageously enables the method described in the present disclosure to be performed by the system with corresponding technical advantages.

Further, the system may comprise means for determining production parameters. For example, the device may be a sensor, such as a barometric pressure sensor, a humidity sensor, or a temperature sensor. The device for determining the production parameter may also be a device for determining the tool used or one of the further production parameters explained above. The device may be in signal and/or data connection with the control and evaluation device.

Further, the system may include an evaluation module, a planning module, and a control module, which are in data connection with each other. In this case, the evaluation module may evaluate the measurement data. In this case, the planning module may determine a measurement strategy for measuring the plurality of components. In particular, this measurement strategy may define the selection of the component to be measured among the plurality of components according to at least one selection parameter. In other words, the measurement policy defines the frequency at which components should be measured, the number of components, and/or optionally which components should also be measured. The measurement policy may also define how the component is measured. For example, one or more test plans may be part of a measurement strategy. The control module may control the selection device and the coordinate measuring machine according to the measurement strategy defined by the planning module.

A system for producing a component is also described, the system comprising a production apparatus and a system for measuring a component according to one of the embodiments described in the present disclosure.

The present invention will be explained in detail based on exemplary embodiments. In the drawings:

figure 1 shows a schematic flow chart of a method for measuring a component according to the invention,

Figure 2 shows a schematic and non-comprehensive illustration of rules for adjusting selection parameters,

figure 3a shows a schematic block diagram for adjusting selection parameters,

figure 3b shows a schematic block diagram for adjusting selection parameters,

figure 4 shows a schematic view of a selection of components to be measured according to a first embodiment,

figure 5 shows a schematic view of a selection of components to be measured according to another embodiment,

figure 6 shows a schematic block diagram of a system according to the invention,

FIG. 7 shows a schematic block diagram of a system according to the invention in another embodiment, an

Fig. 8 shows a schematic flow chart of a method according to the invention according to another embodiment.

Elements having the same reference numerals hereinafter denote elements having the same or similar technical features.

Fig. 1 shows a schematic flow chart of a method according to the invention for measuring a component B (see fig. 4). These components B are produced by the production apparatus 1 (see also fig. 4). The method comprises selecting S1 a component B to be measured from a plurality of produced components B. The selection is made in dependence on at least one selection parameter p. Fig. 1 depicts the selection of the component B to be measured using the initial selection parameter p0 (not adjusted) at the beginning of the method, that is to say before the first adjustment.

In particular, the selection parameters p, p0 may be or represent the sampling frequency. It is conceivable to make the selection in dependence on a plurality of selection parameters p, p0, wherein, for example, a first selection parameter may be or represent the interpreted sampling frequency, and wherein another selection parameter may be the ordinal number of the component B selected from the produced sequence of components B.

There is also creation S2a of component-specific measurement data MD by measuring selected components B using a coordinate measuring machine 2 (see, for example, fig. 4) and evaluation S2B of these measurement data MD created by the measurement. In this case, different coordinate measuring machines can be used, for example a coordinate measuring machine 2 with optical sensors or a coordinate measuring machine 2 with tactile sensors. Naturally, a coordinate measuring machine 2 based on tomographic scanning may also be used.

For example, by evaluating S2B measurement data MD (see for example fig. 3B), at least one component-specific property, in particular a dimensional variable, can thus be determined. This component-specific variable may be a feature of a component-specific test feature, such as a dimensional variable, such as a length, width, diameter, depth, distance from a reference point or line, or any other dimensional variable. Further, component-specific variables representing the quality of the measured component or the quality of the test feature of the measured component B can be determined by evaluating S2B measurement data MD.

Further, the evaluation S2B of the measurement data MD can test whether the component, respectively the measured component B, passes a quality evaluation, in particular whether a predetermined quality criterion of the component B is fulfilled. Such quality assessment, in particular testing of the test standard, may be performed based on component specific characteristics. Therefore, the evaluation result r may be the number of components B that have passed the quality evaluation.

Further, a component-specific characteristic of the or each component B of the selected plurality of components B may be determined, wherein at least one resulting characteristic is then determined as the evaluation result r based on this component-specific characteristic. This may be, for example, or represent a mean or divergence of a particular characteristic of the component.

In the alternative or cumulatively to creating S2a component-specific measurement data MD and evaluating S2b, at least one production parameter m may be determined S3 (see fig. 6). An exemplary production parameter m is still explained in detail below.

Further, at least one selection parameter p, p0 is adjusted S4 based on the evaluation result r and/or based on the production parameter m or a change in the production parameter m.

In this case, the adjustment of the selection parameter p may be carried out in a result-dependent manner instead of in a production parameter-dependent manner. Alternatively, the adjustment S4 of the selection parameter p may be implemented in a production parameter-dependent manner, not in a result-dependent manner. Adjustment S4 may also be related to both the result and to the production parameters.

The result-dependent adjustment S4 may refer to if, for example, the result r

With the predetermined value of the value,

deviate from a predetermined target value by more or not more than a predetermined amount, and/or

Changing in a predetermined manner, an adjustment S4 is implemented.

Furthermore, the quality measure may be determined based on the evaluation of a plurality of or each of the selected components B, for example as a ratio between the number of selected components and the total number of selected components based on which the evaluation has passed the quality evaluation, wherein the result-dependent adjustment S4 is implemented based on the quality measure or a variation thereof.

The adjustment S4 related to the production parameter may refer to if the production parameter m

With the predetermined value of the value,

deviate from a predetermined target value by more or not more than a predetermined amount, and/or

The adjustment S4 is carried out in a predetermined manner, in particular with or without a change during a predetermined period of time.

Naturally, other result-related or production parameter-related adjustments S4 are conceivable as well. In particular, it is conceivable that the adjustment may be performed even if the production parameter m does not change during a predetermined period of time or does not change more than a predetermined amount.

In particular, the adjustment relating to the production parameters can be carried out with a time offset. For example, the adjustment may be performed only a predetermined period after the criteria for adjustment have been met. This has been explained above.

Adjusting the selection parameter p may refer to redefining the value in this way, however, as a result of which the current value of the selection parameter p does not necessarily change. Naturally, however, the value of the selection parameter p may also be changed as a result of the adjustment S4. For example, the adjustment S4 may be implemented by means of determining a change in the currently set selection parameter p, and the adjusted selection parameter p is then determined as the current selection parameter p that has been modified in accordance with the change.

In addition to the creation and evaluation S2a, S2b of the component-specific measurement data MD or in addition to the determination S3 of the at least one production parameter m, at least one measurement parameter can also be determined, wherein the adjustment of the at least one selection parameter is furthermore carried out in a manner dependent on the measurement parameter.

Adjustment S4 is preferably performed in an automated manner. For this purpose, the result r can form an input variable for the adjustment method, the output variable of which is the adjusted selection parameter p. In the alternative, or cumulatively, at least one production parameter p or a variation thereof may form an input variable for the method for adjusting S4 the selection parameter p. In this respect, the explanations set forth above with respect to the adjustment S4 based on the evaluation result r apply accordingly. The adjusted selection parameter p may be determined by an adjustment method, for example, a selection parameter p assigned to the input variable according to a predetermined mapping or a selection parameter generated from the input variable due to a predetermined functional relationship.

The adjustment S4 is preferably carried out in an automatic manner, for example by means of a suitable evaluation device 3 (see fig. 6), which may be implemented, for example, as a microcontroller or an integrated circuit or comprise one of these.

It is further preferred that the selection parameter p is adjusted S4 in a rule-based manner. Exemplary rules R1, R2, rn, rn+1, rm, rm+1 are depicted in fig. 2. The first rule represents a first value sigma 1 of the divergence and a rule-specific selection parameter p _R1 A relationship or a mapping between them.

Thus, for example, the divergence value of a component-specific property can be determined as the resulting property, which forms the input variable for the first rule R1, as the evaluation result R of the measurement data MD. Thus, if this first value σ1 is determined, the selection parameter p is adjusted according to the rule-specific output variable, i.e. the selection parameter p _R1 。

The second rule R2 represents the relation between the second value σ2 of the divergence and the selection parameter p. Thus, if the second value σ2 is determined as the evaluation result r, there is a selection parameter p to rule-specific output variable (i.e., a divergence-specific selection parameter p _R2 ) Is mapped to the mapping of (a).

Thus, one or more rules R1, R2 can represent the relation between a plurality of or even all of the possible values of the divergence and the selection parameter p related to the divergence.

The rule may also determine a change in the selection parameter p, and then determine the adjusted selection parameter p from the change in the selection parameter p thus determined by a change in the currently set selection parameter p.

Also shown is an nth rule Rn representing a first production temperature T1 and a rule-specific selection parameter p _Rn Relationship between them. In this case, the first production temperature T1 forms an exemplary production parameter m. If it is detected that the production temperature corresponds to the first production temperature T1, the selection parameter p is adapted to the corresponding output value p of the nth rule _Rn . The n+1th rule Rn+1 is also depicted. The latter correspondingly represents the second production temperature T2 and the selection parameter p _Rn+1 Relationship between them.

The mth rule Rm is also depicted. The input of the mth ruleThe absolute value of the deviation between the first mean value mu 1 of the characteristic specified by the variable forming means and the target value mu soll and the period between the current time t and the reference time t0 (for example the time of the last implemented adjustment S4 of the selection parameter p). If the two input variables take on a predetermined value, in particular simultaneously, a predetermined value, a rule-specific value p _Rm Is determined as the output parameter and the currently set selection parameter p is adapted to this value.

Also depicted is the m+1th rule Rm+1, the input variables of which are the absolute value of the interpreted deviation and the deviation between the number n (t) of components B produced at the current time t and the number n (t 0) of components B produced at a certain time (e.g. the time at which the adjustment was last performed). If the two input variables adopt a predetermined value, in particular simultaneously, a predetermined value, the specific selection parameter p is correspondingly regulated _Rm+1 May be set as the adjusted selection parameter p.

The exemplary rules R1, R2, …, rn, rn+1, …, rm, rm+1 depicted in fig. 2 can be determined, for example, by machine learning, for example, based on the combined explanatory evaluation of the production parameters m that have been acquired or determined and the evaluation result R of the measurement data MD created by the coordinate measuring machine 2 when measuring the component B produced according to these production parameters m. Further, the depicted rules R1, R2, …, rn, rn+1, …, rm, rm+1 may be adapted, for example, also by an explanatory evaluation of the production parameter m and an evaluation result R of the measurement data MD created by the application according to the existing rules R1, R2, …, rn, rn+1, …, rm, rm+1.

The rules R1, R2, …, rn, rn+1, …, rm, rm+1 depicted in fig. 2 may be determined by a machine learning method. For example, both the production parameter m and the measurement data MD and/or the evaluation result r of these measurement data MD may be acquired and stored within a predetermined period of time. Then, in the data set thus acquired, the relation between the change in the production parameter m and the subsequent change in the measurement data MD or the result r can be analyzed by means of, in particular, a data mining method. Further, selection parameters p can then be determined which ensure a reliable and sufficiently accurate quality assessment of the produced component for the forthcoming result r.

This determination may be implemented based on a mapping known in advance. Thus, for example, selection parameters p can be assigned to certain evaluation results r, which ensure a reliable and sufficiently accurate quality evaluation in the case of these measurement results.

Thus, for example, it may be determined whether a change in one or more production parameters m causes an improvement in the quality of the produced component B, wherein the improvement may be detected, for example, as long as the mean value of the component-specific variable deviates from the target value by less than a predetermined amount and/or the divergence of the component-specific variable is less than a predetermined amount. The selection parameters may then be adjusted accordingly, e.g. the sampling frequency may be reduced.

The production parameter m, the measurement data MD and/or the evaluation results r of these measurement data MD and the adjustment of the at least one selection parameter p by the user can also be acquired and optionally stored during a predetermined period of time. The adjustments made by the user, the evaluation result R and/or the relation between the production parameters p or variations thereof can then be determined by evaluating this dataset and can be used to determine the rules R1, R2, …, rn, rn+1, …, rm, rm+1 depicted in fig. 2.

The captured input variables (i.e. production parameters, in particular the set or given production parameters or changes in production parameters) and the output variables (i.e. at least one selection parameter to be adjusted by adjustment or a change thereof) can thus form training data for determining the model by means of a machine learning method as explained above. This model may then be used to determine output variables for input variables that are different from the input variables of the training data.

The rules may be adjusted accordingly. Thus, it can be detected, for example, whether the user subsequently changes the selection parameter p determined by the rule. If this is the case, the corresponding rule may be adjusted. For example, the models may be relearned or retrained.

Fig. 3a shows a schematic determination of a selection parameter p, which is determined on the basis of an exemplary production parameter m, wherein the currently set selection parameter p can be adjusted in accordance with the selection parameter p thus determined in such a way that the measurement S1 (see fig. 1) can then be carried out in accordance with the adjusted selection parameter p. As production parameter m, a tool W for production is exemplarily shown. The production temperature T is another exemplary production parameter m. The production time t is another exemplary production parameter m, wherein, based on the time, a production period since a predetermined time t0 can be determined.

Another exemplary production parameter m is the number n of produced components B, wherein in particular the number of components B produced since the predetermined time can be determined based on this number n.

The production air pressure D is another exemplary production parameter m.

Another exemplary production parameter m is the ordinal number of the lot Cn, wherein in particular the number of lots C produced since a predetermined time can be determined based on this ordinal number (see fig. 4).

Another exemplary production parameter m is a shift group S, such as early shift, white shift, late shift or night shift.

In this case, the selection parameter p may be determined based on exactly one of the indicated production parameters m or based on a plurality of the indicated production parameters m. Alternatively or cumulatively, the selection parameter p may be determined based on a change of exactly one of the illustrated exemplary production parameters m or based on a change of a plurality of the illustrated production parameters m.

The production parameter m shown can thus form an input variable for determining the selection parameter, in particular in a rule-based manner. The step of determining the production parameter m is not depicted in fig. 3 b.

Fig. 3B depicts an exemplary determination of the selection parameter p based on measurement data MD created by measuring a component B selected according to the set selection parameter p. In this case, the measurement data MD is evaluated S2b, and the evaluation result r is determined. Exemplary results r have been explained above. The selection parameter p is then determined based on this result r. This determination may also be implemented in a rule-based manner. After the determination, the currently set selection parameter p may be adjusted to the value thus determined, thereby implementing the adjustment.

In the embodiment shown in fig. 3a, at the kth component B produced or measured since the predetermined time t0, the selection parameter p may be adjusted, for example, to a predetermined value or changed, in particular increased by a predetermined value (see fig. 2).

When the shift group S changes, i.e. in the case of a shift change, the selection parameter p may also be adjusted to, for example, a predetermined value or be changed, in particular increased by a predetermined value.

In each case, the selection parameter p may also be adjusted, for example, to a predetermined value or changed, in particular increased, by a predetermined value after expiration of a predetermined time interval (for example every 24 hours or every 48 hours).

In the case of a tool change, or a change in the batch C, or in the case of a change in the environmental conditions (e.g. the production temperature T or the production pressure D), the selection parameter p can also be adjusted, for example, to a predetermined value or changed, in particular increased by a predetermined value.

In the case of the determination and adjustment of the selection parameters shown in fig. 3B, the selection parameters p can be changed if the characteristics of the produced component B or the development thereof over time are determined such that a reliable quality assessment can be ensured with the changed selection parameters p.

Fig. 4 shows an exemplary illustration of the selection of a component B for the measurement by the coordinate measuring machine 2. In this case, the component B is produced or manufactured, in particular at least partially manufactured, by the production device 1. Here, the batches Cn, cn+1 are illustrated to comprise 10 components B, wherein only one component B of each batch Cn, cn+1 is provided with a reference numeral for the sake of clarity. The nth batch Cn and the n+1th batch cn+1 are depicted. In this case, the shaded component B represents the selected component B, which should be measured by the coordinate measuring machine 2. Here, the sampling frequency of the nth lot Cn is illustrated as 5/10, every other component B measuring the nth lot Cn.

The sampling frequency of the n+1th batch Cn+1 is 3/10, and every fourth measurement unit B.

Thus, the sampling frequency and the ordinal number of the component B to be selected in the sequence of production components B of the batches Cn, cn+1 are adjusted S4. Fig. 4 illustrates that the sampling frequency is reduced. However, increasing the sampling frequency is naturally also conceivable in the case of batch changes.

Fig. 5 shows an exemplary illustration of a component B to be selected according to another embodiment. In contrast to the embodiment shown in fig. 4, the sampling frequency of the nth batch Cn and the n+1th batch cn+1 is the same. However, the ordinal number of the part B to be measured differs in the sequence of the production parts B of the batches Cn, cn+1. Thus, in the exemplary embodiment shown in fig. 4, the first, third, fifth, seventh and ninth components B of the nth lot Cn are selected, while in the exemplary embodiment shown in fig. 5, the first, second, fourth, seventh and tenth components B are selected. Further, in the exemplary embodiment shown in fig. 4, the first, fifth and ninth components of the n+1 lot cn+1 are selected, while in the exemplary embodiment shown in fig. 5, the first, fourth and eighth components B are selected.

Fig. 6 shows an exemplary block diagram of a system 4 for measuring a plurality of components B (see fig. 4) according to the present invention. The system comprises a coordinate measuring machine 2 and an evaluation and control device, which comprises or is formed by an evaluation module 5, a planning module 3 and a control module 6. A production device 1 is also depicted, which may equally be part of the system 4, but this is not necessary. Also depicted is a device 7, such as a sensor, for determining a production parameter m. The transport device for transporting the produced component B to the coordinate measuring machine 2 is not depicted here.

All or a selected produced component B is supplied to the coordinate measuring machine 2, for example by means of a transport device (not depicted here).

Then, the selected component B is measured by the coordinate measuring machine 2, and measurement data MD is created. Therefore, if all the produced parts B are supplied to the coordinate measuring machine 2, only the selected parts B are measured.

These measurement data MD are transmitted from the coordinate measuring machine 2 to the evaluation module 5. For this purpose, the coordinate measuring machine 2 can be connected to the evaluation module 5 in a data and/or signal connection. The evaluation module 5 can in particular carry out a statistical evaluation of the determined measurement data MD. For example, as explained above, the evaluation module 5 may determine the mean μ1 and/or the divergence σ of the measured component-specific variables of the component B as the evaluation result r.

Such a result r may then be transmitted to the planning module 3. For this purpose, the evaluation module 5 can be connected to the planning module 3 in a data and/or signal connection. The planning module 3 may then make an adjustment S4 of the at least one selection parameter p (see fig. 1) based on the result r.

For example, planning module 3 may determine an adjusted measurement strategy for measuring a plurality of produced components B. The control module 6 may then control the selection and measurement of the component B based on this measurement strategy, for example by controlling the transport device (not shown) and/or the coordinate measuring machine 6. For this purpose, the planning module 3 can be connected to the control module 6 in a data and/or signal connection. The measurement strategy thus defines the number of components B and optionally also which components B are selected from the sequence of produced components B for measurement by the coordinate measuring machine 2. However, the measurement strategy may also define a component-specific test plan by means of which in particular the sensors to be used, the travel of the sensors to be used for the measurement and the component-specific measurement strategy are defined.

The evaluation module 5 may also be integrated into the planning module 3 or both modules 3, 5 may be implemented as a joint module. It is also conceivable for the evaluation module 5 to carry out an adjustment S4 of a selection parameter p on the basis of the result r, wherein this selection parameter p is then transmitted to the planning module 3 and the planning module then determines the adjusted measurement strategy.

The device 7 for determining the production parameter m is also depicted as being connected to the planning module 3 in a data and/or signal connection. The planning module 3 can thus also carry out an adjustment of the at least one selection parameter p on the basis of the production parameter m or a change thereof and determine an adjusted measurement strategy.

It is of course also conceivable that the device 7 for determining the production parameter m is likewise connected in data and/or signal to the evaluation module 5 and that it transmits the production parameter m to this module 5, which can then also make an adjustment of at least one selection parameter p on the basis of the production parameter m or a change thereof and then transmit this selection parameter p to the planning module 3 as explained above.

Fig. 7 shows a schematic block diagram of a system 4 according to the invention in another embodiment. In contrast to the exemplary embodiment shown in fig. 6, it is shown that the planning module 3 can also adjust at least one production parameter p for the production of the component B by the production plant 1, in addition to the adjustment S4 of the at least one selection parameter p (see fig. 1) based on the evaluation result r and/or based on the production parameter m or a change thereof. For this purpose, the planning module 3 can, for example, determine suitable control commands for a device for adjusting target values of production parameters (for example production temperature or production pressure) of the tool W for production, which control commands then adjust the production parameters to the corresponding target values. The adjustment of the production parameter m by the planning module can be carried out in a result-dependent manner, in particular in a purely result-dependent manner, i.e. on the basis of the value or the change of the evaluation result r. This may advantageously effect a timely change of the production process to ensure the quality of the produced component B.

In this case, the planning module 3, the evaluation module 5 and the control module 6 may each comprise a computing device or a data processing device, which may be designed, for example, as a microcontroller or an integrated circuit or comprise one of these. However, the functionality of the modules may naturally also be provided by a common computing device or data processing device.

Fig. 8 shows a schematic flow chart of the method according to the invention in another embodiment. According to the embodiment depicted in fig. 1, a component to be measured is selected S1 from a plurality of components B according to an initial selection parameter p 0. Further, at least one production parameter m is determined S3 and at least one selection parameter p is adjusted S4 based on the production parameter m or a variation thereof. Thus, the adjustment is performed in a purely production parameter dependent manner, not in a result dependent manner.

Further, this purely production parameter-dependent adjustment S4 is followed by a selection S1 of the selection parameters p which have been adjusted in a production parameter-dependent manner, wherein the coordinate measuring machine 2 (see fig. 6) is then used to create S2a component-specific measurement data MD by measuring the correspondingly selected component B and to evaluate S2B measurement data MD. Further, an adjustment S4 of the at least one selection parameter p is then carried out based on the evaluation result r. The adjustment is performed in a purely outcome-dependent manner and not in a production parameter-dependent manner. This is followed by a selection S1 according to the adjusted selection parameter p.

List of reference numerals

1. Production equipment

2. Coordinate measuring machine

3. Planning module

4. System and method for controlling a system

5. Evaluation module

6. Control module

7. Determination device

S1 selection

S2a creation

S2b analysis

S3 determining at least one production parameter

S4 adjustment

p selection parameter

p0 initial selection parameter

m production parameter

r evaluation results

Dispersion of sigma 1 and sigma 2

T, T1 production temperature T2

Mu 1 mean value

Mu soll target mean

time t

t0 for a predetermined time

n (t) ordinal number

n (t 0) ordinal number

R1, R2, rn, rn+1, rm, rm+1 rule

p _R1 、p _R2 、p _Rn 、p _Rn+1 、p _Rm 、p _Rm+1 Rule-specific selection parameters

W tool

n ordinal number

D production pressure

S shift group

Cn nth batch

Cn+1nth+1th batch

B component

Claims (15)

1. A method for measuring a component (B) produced by a production device (1), comprising:

a) Selecting (S1) a component (B) to be measured from a plurality of components (B), the selection being made in dependence on at least one selection parameter (p),

b) Creating (S2 a) component-specific Measurement Data (MD) by measuring the selected component (B) using a coordinate measuring machine (2) and evaluating (S2B) the Measurement Data (MD), and/or

c) Determining (S3) at least one production parameter (m),

d) The at least one selection parameter (p) is adjusted (S4) on the basis of the evaluation result (r) and/or on the basis of the production parameter (m) or a change in the production parameter (m).

2. A method as claimed in claim 1, characterized in that at least one measurement parameter is determined, the at least one selection parameter (p) being adjusted on the basis of the measurement parameter or a change in the measurement parameter.

3. Method according to any one of the preceding claims, characterized in that the at least one selection parameter (p) is adjusted in a partially or fully automated manner.

4. A method as claimed in claim 3, characterized in that the selection parameter (p) is adjusted on the basis of rules.

5. The method of claim 4, wherein the rules are determined by machine learning.

6. The method according to any of the preceding claims, characterized in that the production parameter (m) is or represents an environmental condition, a tool (W) for the production, a method for the production, the number (n) of parts (B) produced since a certain time (t 0), a production period (t) since a certain time (t 0), the number of batches (Cn, cn+1) produced since a certain time, or a shift set (S), and/or the measurement parameter is or represents a sensor for the measurement.

7. A method as claimed in any one of the preceding claims, characterized in that the selection parameter (p) is the sampling frequency.

8. Method according to any one of the preceding claims, characterized in that the at least one selection parameter (p) or the further selection parameter (p) is at least one ordinal number in the sequence of the produced component (B).

9. Method according to any of the preceding claims, characterized in that the parts (B) to be measured are selected from parts (B) of a batch (Cn), wherein the parts (B) to be measured are selected from parts (B) of another batch (cn+1) according to the adjusted selection parameter (p).

10. The method according to any of the preceding claims, characterized in that at least one quality measure of a plurality of or each of the selected components (B) is determined by the evaluation (S2B), wherein the adjustment (S4) of the at least one selection parameter (p) is carried out on the basis of the quality measure or a change in the quality measure.

11. Method according to any of the preceding claims, characterized in that at least one component-specific characteristic of a plurality of or each component (B) of the selected component (B) is determined by the evaluation (S2B), wherein,

a) If a predetermined number of these measured component-specific properties of component (B) deviate from the target value by more or less than a predetermined amount, or if the component-specific properties change by more or less than a predetermined amount, an adjustment is made, or

b) At least one resulting characteristic is determined based on the component-specific characteristics, and an adjustment is performed if the resulting characteristic deviates from the target value by more than or less than a predetermined amount, or if the resulting characteristic varies by more than or less than a predetermined amount.

12. Method according to any of the preceding claims, characterized in that after the determination of the at least one production parameter (m), the selection parameter (p) is set to the value assigned to the production parameter (m) or to the value assigned to the change of the production parameter (m) or the selection parameter (p) is changed, which change is assigned to the production parameter (p) or to the change of the production parameter (p).

13. A method according to any of the preceding claims, characterized in that the purely production parameter-dependent adjustment of the at least one selection parameter (p) is followed by

a) Selecting (S1) a component (B) to be measured from a predetermined number of components (B), the selection being made on the basis of a selection parameter (p) which has been adjusted in a manner related to the production parameter,

b) Creating (S2 a) component-specific Measurement Data (MD) by measuring the selected component (B) using a coordinate measuring machine (2) and analyzing (S2B) the Measurement Data (MD), and

c) Based on the evaluation result (r), the at least one selection parameter (p) is updated and a purely result-dependent adjustment (S4) is carried out.

14. A program which, when executed on or by a computer, prompts the computer to perform one or more or all of the steps of the measurement method of any one of claims 1 to 13.

15. A system for measuring a component (B) produced by a production device (1), comprising at least one coordinate measuring machine (2) and at least one evaluation and control device, wherein the system (4) is configured to perform the steps of:

a) Selecting (S1) a component (B) to be measured from a plurality of components (B), the selection being made in dependence on at least one selection parameter (p),

b) Creating (S2 a) component-specific Measurement Data (MD) by measuring the selected component (B) using a coordinate measuring machine (2) and evaluating (S2B) the Measurement Data (MD), and/or

c) Determining (S3) at least one production parameter (p),

d) The at least one selection parameter (p) is adjusted (S4) on the basis of the evaluation result (r) and/or on the basis of the production parameter (p) or a change in the production parameter (r).